SoroushVahidi/scidocs huggingface dataset:
Patent classifications as indicators of intellectual organization
(2008)

Using the 138,751 patents filed in 2006 under the Patent Cooperation Treaty, coclassification analysis is pursued on the basis of threeand four-digit codes in the International Patent Classification (IPC, 8 edition). The co-classifications among the patents enable us to analyze and visualize the relations among technologies at different levels of aggregation. The hypothesis that classifications might be considered as the organizers of patents into classes, and that therefore co-classification patterns—more than co-citation patterns—might be useful for mapping, is not corroborated. The classifications hang weakly together, even at the four-digit level; at the country level, more specificity can be made visible. However, countries are not the appropriate units of analysis because patent portfolios are largely similar in many advanced countries in terms of the classes attributed. Instead of classes, one may wish to explore the mapping of title words as a better approach to visualize the intellectual organization of patents.
Genetically Improved PSO Algorithm for Efficient Data Clustering
(2010)

Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this paper, we present a hybrid two-phase GAI-PSO+k-means data clustering algorithm that performs fast data clustering and can avoid premature convergence to local optima. In the first phase we utilize the new genetically improved particle swarm optimization algorithm (GAI-PSO) which is a population-based heuristic search technique modeled on the hybrid of cultural and social rules derived from the analysis of the swarm intelligence (PSO) and the concepts of natural selection and evolution (GA). The GAI-PSO combines the standard velocity and position update rules of PSOs with the ideas of selection, mutation and crossover from GAs. The GAI-PSO algorithm searches the solution space to find the optimal initial cluster centroids for the next phase. The second phase is a local refining stage utilizing the k-means algorithm which can efficiently converge to the optimal solution. The proposed algorithm combines the ability of the globalized searching of the evolutionary algorithms and the fast convergence of the k-means algorithm and can avoid the drawback of both. The performance of the proposed algorithm is evaluated through several benchmark datasets. The experimental results show that the proposed algorithm is highly forceful and outperforms the previous approaches such as SA, ACO, PSO and k-means for the partitional clustering problem.
s t )-r technologies social responses to communication c Defining Virtual Reality : Dimensions Determining Telepresence
(1992)

Virtual reality (VR) is typically defined in terms of technological hardware. This paper attempts to cast a new, variable-based definition of virtual reality that can be used to classify virtual reality in relation to other media. The defintion of virtual reality is based on concepts of “presence” and “telepresence,” which refer to the sense of being in an environment, generated by natural or mediated means, respectively. Two technological dimensions that contribute to telepresence, vividness and interactivity, are discussed. A variety of media are classified according to these dimensions. Suggestions are made for the application of the new definition of virtual reality within the field of communication research.
Advances in ultrasound biomicroscopy.
(2000)

The visualisation of living tissues at microscopic resolution is attracting attention in several fields. In medicine, the goals are to image healthy and diseased tissue with the aim of providing information previously only available from biopsy samples. In basic biology, the goal may be to image biological models of human disease or to conduct longitudinal studies of small-animal development. High-frequency ultrasonic imaging (ultrasound biomicroscopy) offers unique advantages for these applications. In this paper, the development of ultrasound biomicroscopy is reviewed. Aspects of transducer development, systems design and tissue properties are presented to provide a foundation for medical and biological applications. The majority of applications appear to be developing in the 40-60-MHz frequency range, where resolution on the order of 50 microm can be achieved. Doppler processing in this frequency range is beginning to emerge and some examples of current achievements will be highlighted. The current state of the art is reviewed for medical applications in ophthalmology, intravascular ultrasound, dermatology, and cartilage imaging. Ultrasound biomicroscopic studies of mouse embryonic development and tumour biology are presented. Speculation on the continuing evolution of ultrasound biomicroscopy will be discussed.
A practical approach for content mining of Tweets.
(2013)

Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.
60 GHz system-on-package antenna array with parasitic microstrip antenna single element
(2008)

In this paper microstrip antenna for 60 GHz on LTCC is presented. Special techniques are used to satisfy the antenna specification in wide bandwidth (7 GHz, from 57 GHz to 64 GHz) and high gain (15 dBi). An increase in bandwidth is achieved by using aperture the coupling and the multilayer structure. Gain is increased by using parasitic patches which increase the total antenna aperture. In paper was presented 60 GHz parasitic microstrip antenna with two layers of parasitic patches. High gain is obtained as the result of the optimization. However, presented return loss results can not be treated as wide bandwidth.
Checking Business Process Evolution
(2016)

Business processes support the modeling and the implementation of software as workflows of local and inter-process activities. Taking over structuring and composition, evolution has become a central concern in software development. We advocate it should be taken into account as soon as the modeling of business processes, which can thereafter be made executable using process engines or model-to-code transformations. We show here that business process evolution needs formal analysis in order to compare different versions of processes, identify precisely the differences between them, and ensure the desired consistency. To reach this objective, we first present a model transformation from the BPMN standard notation to the LNT process algebra. We then propose a set of relations for comparing business processes at the formal model level. With reference to related work, we propose a richer set of comparison primitives supporting renaming, refinement, propertyand context-awareness. Thanks to an implementation of our approach that can be used through a Web application, we put the checking of evolution within the reach of business process designers.
On the number of response regions of deep feed forward networks with piece-wise linear activations
(2013)

This paper explores the complexity of deep feedforward networks with linear presynaptic couplings and rectified linear activations. This is a contribution to the growing body of work contrasting the representational power of deep and shallow network architectures. In particular, we offer a framework for comparing deep and shallow models that belong to the family of piecewise linear functions based on computational geometry. We look at a deep rectifier multi-layer perceptron (MLP) with linear outputs units and compare it with a single layer version of the model. In the asymptotic regime, when the number of inputs stays constant, if the shallow model has kn hidden units and n0 inputs, then the number of linear regions is O(k0n0). For a k layer model with n hidden units on each layer it is Ω(bn/n0c n0). The number bn/n0c grows faster than k0 when n tends to infinity or when k tends to infinity and n ≥ 2n0. Additionally, even when k is small, if we restrict n to be 2n0, we can show that a deep model has considerably more linear regions that a shallow one. We consider this as a first step towards understanding the complexity of these models and specifically towards providing suitable mathematical tools for future analysis.
Business Process Design by Reusing Business Process Fragments from the Cloud
(2015)

The constant development of technologies forces companies to be more innovative in order to stay competitive. In fact, designing a process from scratch is time consuming, error prone and costly. In this context, companies are heading to reuse process fragments when designing a new process to ensure a high degree of efficiency, with respect to delivery deadlines. However, reusing these fragments may disclose sensitive business activities, especially if these latter are deployed in an untrusted environment. In addition, companies are concerned about their user's privacy. To address these issues, we investigate how to build a new business process by reusing the safest existing fragments coming from various cloud servers, i.e. The ones that comply at best with company's preferences and policies, and offer an appropriate level of safety.
A Communication Theoretical Analysis of Multiple-Access Channel Capacity in Magneto-Inductive Wireless Networks
(2017)

Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.
A Space Vector Modulation Scheme of the Quasi-Z-Source Three-Level T-Type Inverter for Common-Mode Voltage Reduction
(2018)

The conventional three-level inverter suffers the limitation of voltage buck operation. In order to give both voltage buck and boost operation capability, the quasi-Z-source three-level T-type inverter (3LT<inline-formula> <tex-math notation="LaTeX">$^2$</tex-math></inline-formula>I) has been proposed. This paper further proposes a space vector modulation (SVM) scheme for the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX">$^2$</tex-math> </inline-formula>I to reduce the magnitude and slew rate of common-mode voltage (CMV). By properly selecting the shoot-through phase, the shoot-through states are inserted within zero vector in order not to affect the active states and output voltage. Doing so, the CMV generated by the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX"> $^2$</tex-math></inline-formula>I is restricted within one-sixth of dc-link voltage, and voltage boosting and CMV reduction can be simultaneously realized. In addition, high dc-link voltage utilization can be maintained. The proposed scheme has been verified in both simulations and experiments. Comparisons are conducted with the conventional SVM method and the phase-shifted sinusoidal PWM method.
User experience: challenges and opportunities
(2013)

User expectations, motivations and feelings when using a product or encountering a system have urged the need to investigate beyond the traditional functionality and usability concerns by assessing and designing for the user experience. Having this realized, we can ensure positive user experience as well as desirable products. This paper aims at providing an overview of the main concepts and principles related to user experience. The topics discussed in this paper cover both theories from the academia as well as empirical studies from the industry. Due to misconception of user experience, it was found that industry depends on and uses the traditional usability methods in the product development as user experience practices. As a result, this paper discusses and elaborates the relationship between conventional usability and user experience in addition to the variety of methods which can be used for user experience evaluation during different time spans. Furthermore, user experience extends over different areas such as human computer interaction, and product design and development. Knowledge and concepts related to user experience are scattered due to the dynamic concepts of user experience. In other words, authors tend to discuss the user experience from their own perspective and interest. Hence, this paper is trying to provide and establish a holistic understanding of the user experience by covering different topics related to user experience. The output of this paper may provide and assist researchers and practitioners to establish a base to aid in proper understanding of user experience, its concepts and principles.
High performance communication architecture for smart distribution power grid in developing nations
(2018)

In a smart distribution power grid, cost efficient and reliable communication architecture plays a crucial role in achieving complete functionality. There are different sets of Quality of Services (QoS) requirements for different data packets transmitting inside the microgrid (a regionally limited smart distribution grid), making it challenging to derive optimal communication architecture. The objective of this research work is to determine the optimal communication technologies for each data packet based on its QoS requirement. In this paper, we have proposed an architecture for a smart distribution power grid with Cyber Physical System enabled microgrids, which accommodate almost all functional requirements of a smart distribution power grid. For easy transition towards optimal communication architecture, we have presented a six-tier communication topology, which is derived from the architecture for a smart distribution power grid. The optimization formulations for each packet structure presented in this paper minimize the overall cost and consider the QoS requirements for each packet. Based on the simulation results, we have made recommendations for optimal communication technologies for each packet and thereby developed a heterogeneous communication architecture for
Sexual Dimorphism in the Parietal Substrate Associated with Visuospatial Cognition Independent of General Intelligence
(2010)

Sex differences in visuospatial cognition (VSC) with male advantage are frequently reported in the literature. There is evidence for sexual dimorphisms in the human brain, one of which postulates more gray matter (GM) in females and more white matter (WM) in males relative to total intracranial volume. We investigated the neuroanatomy of VSC independent of general intelligence (g) in sex-separated populations, homogenous in age, education, memory performance, a memory- and brain morphology-related gene, and g. VSC and g were assessed with the Wechsler adult intelligence scale. The influence of g on VSC was removed using a hierarchical factor analysis and the Schmid–Leiman solution. Structural high-resolution magnetic resonance images were acquired and analyzed with voxel-based morphometry. As hypothesized, the clusters of positive correlations between local volumes and VSC performance independent of g were found mainly in parietal areas, but also in pre- and postcentral regions, predominantly in the WM in males, whereas in females these correlations were located in parietal and superior temporal areas, predominantly in the GM. Our results suggest that VSC depends more strongly on parietal WM structures in males and on parietal GM structures in females. This sex difference might have to do with the increased axonal and decreased somatodendritic tissue in males relative to females. Whether such sex-specific implementations of the VSC network can be explained genetically as suggested in investigations into the Turner syndrome or as a result of structural neural plasticity upon different experience and usage remains to be shown.
The effect of flow experience on mobile SNS users' loyalty
(2010)

Purpose – The purpose of this research is to examine the effect of flow experience on mobile social networking service (SNS) users’ loyalty. Design/methodology/approach – Based on 305 valid responses collected from a survey questionnaire, structural equation modeling (SEM) technology was employed to examine the research model. Findings – The results show that both information quality and system quality significantly affect users’ trust and flow experiences, which further determine their loyalty. The results indicate that flow experience is the strongest determinant of users’ loyalty. Practical implications – Mobile SNS providers need to consider user experience when seeking users’ loyalty. They should enhance information quality and system quality in order to improve user trust and flow experience. Originality/value – Although much research has been conducted to explore the effects of extrinsic motivations, such as perceived usefulness, on mobile commerce user behavior, the effect of intrinsic motivations, such as flow experience, is seldom tested. This research found the effect of flow experience on mobile SNS users’ loyalty to be significant.
FingerScanner: Embedding a Fingerprint Scanner in a Raspberry Pi
(2016)

Nowadays, researchers are paying increasing attention to embedding systems. Cost reduction has lead to an increase in the number of platforms supporting the operating system Linux, jointly with the Raspberry Pi motherboard. Thus, embedding devices on Raspberry-Linux systems is a goal in order to make competitive commercial products. This paper presents a low-cost fingerprint recognition system embedded into a Raspberry Pi with Linux.
Depth driven people counting using deep region proposal network
(2017)

People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.
A Sparse Matrix Library in C + + for High PerformanceArchitectures
(1994)

We describe an object oriented sparse matrix library in C++ built upon the Level 3 Sparse BLAS proposal [5] for portability and performance across a wide class of machine architectures. The C++ library includes algorithms for various iterative methods and supports the most common sparse data storage formats used in practice. Besides simplifying the subroutine interface, the object oriented design allows the same driving code to be used for various sparse matrix formats, thus addressing many of the di culties encountered with the typical approach to sparse matrix libraries. We emphasize the fundamental design issues of the C++ sparse matrix classes and illustrate their usage with the preconditioned conjugate gradient (PCG) method as an example. Performance results illustrate that these codes are competitive with optimized Fortran 77. We discuss the impact of our design on elegance, performance, maintainability, portability, and robustness.
Explaining and Harnessing Adversarial Examples
(2015)

Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks’ vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.
Towards Deep Neural Network Architectures Robust to Adversarial Examples
(2014)

Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs. We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs). We find that DAEs can remove substantial amounts of the adversarial noise. However, when stacking the DAE with the original DNN, the resulting network can again be attacked by new adversarial examples with even smaller distortion. As a solution, we propose Deep Contractive Network, a model with a new end-to-end training procedure that includes a smoothness penalty inspired by the contractive autoencoder (CAE). This increases the network robustness to adversarial examples, without a significant performance penalty.